Spatial-temporal local contrast for moving point target detection in space-based infrared imaging system

Zhao, Bo; Xiao, Shanzhu; Lü, Huanzhang; Wu, Dongya

doi:10.1016/j.infrared.2018.10.011

Cited by 27 publications

(16 citation statements)

References 21 publications

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“…The value of l was set to two after parameter analysis conducted in Section 4. To evaluate the detection effectiveness, LSMs are compared with seven state-of-the-art detection methods, including fusion saliency map (FSM) [10], double-neighborhood gradient method (DNGM) [11], neighborhood saliency map (NSM) [14], spatial-temporal local contrast filter (STLCF) [15], spatial-temporal local contrast method (STLCM) [2], spatialtemporal joint processing model (STJP) [28], and multiscale local target characteristics algorithm (MLTC) [29]. NSM, STLCF, STLCM, and STJP are existing space-based detection methods, FSM is a newly proposed detection method utilized for low-altitude slow target detection that has a similar background to space-based detection, and DNGM and MLTC are new detection methods proposed in 2020 and 2021, respectively.…”

Section: Experimental Condition and Evaluation Indexmentioning

confidence: 99%

“…The task of aerial target detection is of great importance in many fields, including air traffic surveillance [1] and intelligence reconnaissance [2]. Space-based infrared (IR) imaging technology has the advantages of all-day and wide-area imaging, while both the on-orbit experiment [3] and ground theoretical research [4] have certified that an aerial target can be detected by space-based IR detectors.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, a great number of LCM-based methods [10][11][12] working on the ground-based platform have subsequently been proposed and detect small targets under complex backgrounds. Moreover, most space-based detection methods, such as local blob-like contrast map and local gradient map (LBCM-LGM) [13], neighborhood saliency map (NSM) [14], spatial-temporal local contrast method (STLCM) [2], and spatialtemporal local contrast filter (STLCF) [15], are LCM based. Though these methods perform well on weak target enhancement, they suppress the strong clutter inefficiently, leading to false alarms.…”

Section: Introductionmentioning

confidence: 99%

“…In 2018, a single-frame method called neighboring saliency map (NSM) for space-based detection was proposed and detected a dim target with a signal-to-clutter (SCR) less than 1. The space-based detection methods based on spatial-temporal local contrast maps (STLCF) [15] and spatial-temporal local contrast maps (STLCM) [2] are both LCM-based methods. Lv et al proposed a method that detects the space-based weak moving target with an SCR ≈ 1 or even <1 [27].…”

Section: Introductionmentioning

confidence: 99%

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Local Spatial–Temporal Matching Method for Space-Based Infrared Aerial Target Detection

Chen

Rao

Chen

et al. 2022

Sensors

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The feature of a space-based infrared signal is that the intensity of clutter is much stronger than that of an aerial target. Such a feature poses a great challenge to aerial target detection since the existing infrared target detection methods are prone to enhance clutter but ignore the real target, which results in missed detection and false alarms. To tackle the challenge, we propose a concise method based on local spatial–temporal matching (LSM). Specifically, LSM mainly consists of local normalization, local direction matching, spatial–temporal joint model, and inverse matching. Local normalization aims to enhance the target to the same strength as the clutter, so that the weak target will not be ignored. After normalization, a direction-matching step is applied to estimate the moving direction of the background between the basic frame and referenced frame. Then the spatial–temporal joint model is constructed to enhance the target and suppress strong clutter. Similarly, inverse matching is conducted to further enhance the target. Finally, a salience map is obtained, on which the aerial target is extracted by the adaptive threshold segmentation. Experiments conducted on four space-based infrared datasets indicate that LSM handles the above challenge and outperforms seven state-of-the-art methods in space-based infrared aerial target detection.

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Section: Experimental Condition and Evaluation Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Local Spatial–Temporal Matching Method for Space-Based Infrared Aerial Target Detection

Chen

Rao

Chen

et al. 2022

Sensors

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show abstract

“…Both TVPCF and STLCF extract the local contrast of moving objects by calculating the intensity difference between the average intensity of one frame and its adjacent frames, so they require much computing time. Subsequently, Zhao et al [15] developed the spatial-temporal local contrast map (STLCM) using a mean filter to predict the predicted values of eight directions, which operates in different directions and extracts the target from the background to obtain the grayscale difference of the spatial domain. STLCM first proposed a method using interval frames instead of the traditional adjacent frame method, effectively improving computing performance in the temporal domain.…”

Section: Introductionmentioning

confidence: 99%

Fast Infrared Small Target Detection by Using Hadamard Product for Spatial-Temporal Matrices

Lan

Chou

et al. 2022

IEEE Access

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Infrared (IR) small target detection has recently been widely used in civil and military applications. In IR small target detection, IR images probed at long distances are easily disturbed by complex backgrounds, light changes, and other noises, which makes it pretty challenging. Most existing literature focuses on low false alarms and high detection accuracy rather than computational efficiency. We propose a fast and reliable IR small target detection method that deals with spatial and temporal domains based on the Hadamard product for spatial-temporal matrices (HPSTM). In the spatial domain, a weighted tri-layer window is used to convolute the IR image and obtain the gradient matrices. In the temporal domain, three interval frames were used instead of conventional adjacent frames and contrast matrices were extracted. Finally, the two spatial-temporal matrices use the Hadamard product to multiply and a simple threshold to transfer into a binary image for target detection. We also proposed the optimal mask size selection (OMSS) method, which can adjust the optimal tri-layer window size in the spatial domain to obtain the best detection result. The experimental results show that the proposed HPSTM method has a high detection accuracy and fast running time compared with other methods, indicating that the proposed HPSTM method is reliable and suitable for real-time IR small target detection.INDEX TERMS Infrared small target detection, spatial and temporal domains, Hadamard product, weighted tri-layer window, real-time.

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Small Moving Target Detection in Infrared Sequences by Using the Multiscale Temporal Relative Local Contrast

Han

Zhang

Jiang

et al. 2021

Lecture Notes in Electrical Engineering

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Spatial-temporal local contrast for moving point target detection in space-based infrared imaging system

Cited by 27 publications

References 21 publications

Local Spatial–Temporal Matching Method for Space-Based Infrared Aerial Target Detection

Local Spatial–Temporal Matching Method for Space-Based Infrared Aerial Target Detection

Fast Infrared Small Target Detection by Using Hadamard Product for Spatial-Temporal Matrices

Small Moving Target Detection in Infrared Sequences by Using the Multiscale Temporal Relative Local Contrast

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